Chrono-Behavioral Fingerprinting for Workforce Optimization
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I3P110Keywords:
Chrono-behavioral fingerprinting, workforce optimization, productivity rhythms, task-switching behavior, behavioral drift tracking, time-signature modeling, machine learning in human resources, role-person fit alignment, personalized performance coaching, burnout prediction systemsAbstract
This article introduces the concept of chrono-behavioral fingerprinting, a novel framework meant to identify their individualized temporal fingerprints matching optimum performance, thereby interpreting & improving more distinct work rhythms. Using a combination of digital time-tracking records, physiological signals (such as heart rate variability or sleep data) & productivity figures, the system generates a unique behavioral profile for every employee. Sequential modelling and clustering are two advanced machine learning methods that find trends suggesting not just ideal work periods for people but also the differences in their attention, energy, and production across the day and week. The result is a strong body of knowledge that supports real-time recommendations including ideal time intervals for concentrated work, job transition signals, and assigned recovery periods to prevent fatigue. Customized dashboards provide managers and employees a clear view of important areas and potential warning signs such burnout indicators prior to their escalation. The goal is not to enforce rigid rules but rather to provide individuals data-informed liberty thereby enabling a more flexible and sympathetic approach to manufacturing. First applications of this approach have shown better time management, greater harmony between personal rhythms & team goals & more job satisfaction. In a fast changing digital environment, chrono-behavioral fingerprinting offers a fresh approach to workforce optimization that respects individual diversity, supports intelligent work practices & creates healthier, more flexible companies
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